在此篇論文中，我們提出了一個基於線上遞迴式獨立成分分析以及眼動雜訊自動去除機制之即時多通道腦波擷取系統晶片設計。腦波訊號是非常微弱的電訊號，故在擷取腦波的過程，經常受到非腦波成份的電訊號干擾。前人利用獨立通道成分分析演算法從一段時間的腦波訊號中萃取出包含於腦波訊號中的雜訊。經過獨立通道成分分析的處理過後，我們可以去除含有雜訊的通道，來重建出不含非腦內成分的腦波訊號。
近來，腦機介面(BCI)蓬勃發展。這類的介面使人可以經過大腦直接控制機器。為了增加此類介面的發展性，以及其運作的穩定性、正確性，即時的擷取到不含非腦內成分的腦電訊號是個重要的課題。
線上遞迴式獨立成分分析這樣的演算法，提供了我們一個即時雜訊萃取的機制，此獨立通道成分分析演算法能在每次腦波訊號由前端擷取器完成取樣後，隨即完成獨立通道成分分析的演算，因此非腦內成分的雜訊能夠及時的被萃取出來。因為這樣即時萃取雜訊的特性，在此設計中我們使用了這個演算法來增加此系統的即時性。
干擾腦波擷取的雜訊大致上可分為兩類：人類自發性的電波干擾，以及外在環境的電雜訊干擾。由於眼睛相當靠近大腦的位置，故在這些干擾中，又以眨眼時所造成的電波干擾最強。因此在此設計中，我們針對這樣的干擾進行去除。現今的演算法中，雖然具備了自動去除眨眼雜訊的機制，但無法使我們完全利用到線上遞迴式獨立成分分析的即時性。在此設計中，我們利用了前人提出的演算法，對演算的流程進行修改，使其能完全搭配線上遞迴式獨立通道成分分析進行即時的眨眼訊號去除。另外，前人所提出的演算方式，可能因為眨眼訊號處在邊界時出現判斷失誤的情況。而我們所提出的即時運算流程，亦解決了此類的判斷失誤。
此設計已實現於TSMC 90 nm COMS 製程，其核心所占的面積為1200 × 1200 μm2。由於共用資源的提供，使得此項設計晶片面積的使用效率比起先前的設計更加突出。也由於使用了及時的運算機制，每個腦波訊號能於其取樣後的0.2638秒內得到不含眨眼雜訊的腦波訊號，在此論文中我們亦提供了評估此系統效能的方式，另外我們亦使用真實的腦波進行處理，結果顯示，經過此系統的處理後，眨眼所造成的肌電雜訊確實的被去除。This thesis presents a system on chip design of online recursive ICA based real-time multi-channel EEG acquisition system with automatic eye blink artifacts rejection. EEG signal is one of the feeblest physiological electrical signals. It is easily contaminated by artifacts caused by noncerebral electrical activity. Previously, ICA was used to extract artifacts from a time period of EEG data. After processing of ICA, automatic artifact detection and elimination were performed. Then, artifact free EEG signals can be reconstructed.
Recently, brain computer interfaces (BCIs) are developed to control machines through EEG directly. In order to enhance the feasibility, reliability, and accuracy of BCIs, EEG signals used for BCI applications should be acquired from human without artifacts in real-time.
For the real-time requirement, online recursive ICA (ORICA) is adopted for real-time artifacts extraction because it can immediately find the ICA result right after each EEG sample.
There are two kinds of artifacts. The one which is caused from the inside of the human body is called as biological artifacts. The other one which is caused from outside of the human body is named as environment artifacts. Since the eyes of human are very close to brain, eye blink artifact is one of the most harmful artifact to EEG signals. Therefore, in this work we focus on automatic eye blink artifact elimination and the algorithm used for eye blink artifact detection is sample entropy. In order to fully take advantage of ORICA, the real-time processing flow is proposed to automatically remove the eye blink artifact without detection misses in real-time.
The system with these algorithms and the proposed real-time processing flow are implemented on a chip using TSMC 90nm CMOS technology. Since the good hardware sharing arrangement, the core size, which is 1200 × 1200 μm2, is lower than previous work even though containing additional eye blink artifact rejection. With the proposed real-time processing flow, artifact free EEG signals are acquired in 0.2638 s after each EEG sample. The performance of eye blink artifact elimination is evaluated through correlation coefficient between original artifact free EEG signals and processed artifact free EEG signal which is 0.9135 on average. The processed results with real EEG signals are also provided and shown to remove eye blink artifacts exactly.